On A Responsive Replenishment System: A Fuzzy Logic Approach

In today's competitive business environment, it is important that customers are able to obtain the preferred items in the shops they visit, particularly for the convenience store chains such as 7-Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that the stocks must be kept to a minimum and at the same time large varieties of popular items are also available for customers. This paper attempts to propose a responsive replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost-effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores.

[1]  Luis Magdalena,et al.  A Fuzzy logic controller with learning through the evolution of its knowledge base , 1997, Int. J. Approx. Reason..

[2]  Paul P. Wang,et al.  Intelligent system to support judgmental business forecasting: the case of estimating hotel room demand , 2000, IEEE Trans. Fuzzy Syst..

[3]  Sheng-Chai Chi,et al.  Examination of the influence of fuzzy analytic hierarchy process in the development of an intelligent location selection support system of convenience store , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[4]  Wlodzislaw Duch,et al.  A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[5]  Henry C. W. Lau,et al.  Development of a fuzzy push delivery scheme for Internet sites , 1999, Expert Syst. J. Knowl. Eng..

[6]  Robert Babuska,et al.  Fuzzy Logic Control: Advances in Applications , 1999 .

[7]  Christine W. Chan,et al.  A hybrid intelligent system architecture for utility demand forecasting , 1997, CCECE '97. Canadian Conference on Electrical and Computer Engineering. Engineering Innovation: Voyage of Discovery. Conference Proceedings.

[8]  João F. Martins,et al.  New trends in recognizing experimental drives: fuzzy logic and formal language theories , 2001, IEEE Trans. Fuzzy Syst..

[9]  P. Kotler.  Marketing management / Philip Kotler , 2003 .

[10]  Eulalia Szmidt,et al.  Fuzzy thinking. The new science of fuzzy logic , 1996 .

[11]  J. F. Balmat,et al.  A fuzzy logic knowledge-based system in naval decision-support aids , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[12]  Henry C. W. Lau.  Neural-fuzzy modeling of plastic injection molding machine for intelligent control , 1999 .

[13]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..